A one-sample location test based on weighted averaging of two test statistics when the dimension and the sample size are large

DOI10.1080/03610926.2015.1066812zbMath1368.62150arXiv1405.2370OpenAlexW2344823191MaRDI QIDQ4976231

Takahiro Nishiyama, Masashi Hyodo

Publication date: 27 July 2017

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1405.2370

zbMATH Keywords

high-dimensional dataasymptotic power\(T^2\)-statisticone-sample location testDempster's test

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Hypothesis testing in multivariate analysis (62H15)

Cites Work

• Testing linear hypotheses of mean vectors for high-dimension data with unequal covariance matrices
• A test for the mean vector with fewer observations than the dimension
• Multiple Comparisons Among Mean Vectors When the Dimension is Larger Than the Total Sample Size
• Asymptotic Results of a High Dimensional MANOVA Test and Power Comparison When the Dimension is Large Compared to the Sample Size
• Multivariate Theory for Analyzing High Dimensional Data
• A Significance Test for the Separation of Two Highly Multivariate Small Samples
• A High Dimensional Two Sample Significance Test